RNN做cifar-10图像分类任务pytorch版本的代码
时间: 2023-12-25 18:05:28 浏览: 64
以下是使用PyTorch实现的RNN模型进行CIFAR-10图像分类的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义超参数
input_size = 32
hidden_size = 100
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 5
learning_rate = 0.001
# 加载数据集并进行预处理
transform = transforms.Compose(
[transforms.Resize((input_size, input_size)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
shuffle=True)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
shuffle=False)
# 定义RNN模型
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x):
# 初始化隐状态
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
# 前向传播RNN模型
out, _ = self.rnn(x, h0)
# 解码最后一个状态
out = self.fc(out[:, -1, :])
return out
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, input_size, input_size).to(device)
labels = labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 测试模型
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, input_size, input_size).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
```
此代码使用了PyTorch内置的RNN模块,并将其应用于CIFAR-10图像分类任务。在训练过程中,我们使用Adam优化器和交叉熵损失函数。在测试过程中,我们计算测试集上的准确率。
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